Accurate identification of individuals with subjective cognitive decline using 3D regional fractal dimensions on structural magnetic resonance imaging.

BACKGROUND AND OBJECTIVE: Accurate identification of individuals with subjective cognitive decline (SCD) is crucial for early intervention and prevention of neurodegenerative diseases. Fractal dimensionality (FD) has emerged as a robust and replicable measure, surpassing traditional geometric metrics, in characterizing the intricate fractal geometrical properties of brain structure. Nevertheless, the effectiveness of FD in identifying individuals with SCD remains largely unclear. A 3D regional FD method can be suggested to characterize and quantify the spatial complexity of the precise gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. METHODS: This study introduces a novel integer ratio based 3D box-counting fractal analysis (IRBCFA) to quantify regional fractal dimensions (FDs) in structural magnetic resonance imaging (MRI) data. The innovative method overcomes limitations of conventional box-counting techniques by accommodating arbitrary box sizes, thereby enhancing the precision of FD estimation in small, yet neurologically significant, brain regions. RESULTS: The application of IRBCFA to two publicly available datasets, OASIS-3 and ADNI, consisting of 520 and 180 subjects, respectively. The method identified discriminative regions of interest (ROIs) predominantly within the limbic system, fronto-parietal region, occipito-temporal region, and basal ganglia-thalamus region. These ROIs exhibited significant correlations with cognitive functions, including executive functioning, memory, social cognition, and sensory perception, suggesting their potential as neuroimaging markers for SCD. The identification model trained on these ROIs demonstrated exceptional performance achieving over 93 % accuracy on the discovery dataset and exceeding 87 % on the independent testing dataset. Furthermore, an exchange experiment between datasets revealed a substantial overlap in discriminative ROIs, highlighting the robustness of our method across diverse populations. CONCLUSION: Our findings indicate that IRBCFA can serve as a valuable tool for quantifying the spatial complexity of gray matter, providing insights into cognitive aging and aiding in the automated identification of individuals with SCD. The demonstrated generalizability and robustness of this method position it as a promising tool for neurodegenerative disease research and offer potential for clinical applications.

Capturing the Individual Deviations From Normative Models of Brain Structure for Depression Diagnosis and Treatment.

BACKGROUND: The high heterogeneity of depression prevents us from obtaining reproducible and definite anatomical maps of brain structural changes associated with the disorder, which limits the individualized diagnosis and treatment of patients. In this study, we investigated the clinical issues related to depression according to individual deviations from normative ranges of gray matter volume. METHODS: We enrolled 1092 participants, including 187 patients with depression and 905 healthy control participants. Structural magnetic resonance imaging data of healthy control participants from the Human Connectome Project (n = 510) and REST-meta-MDD Project (n = 229) were used to establish a normative model across the life span in adults 18 to 65 years old for each brain region. Deviations from the normative range for 187 patients and 166 healthy control participants recruited from two local hospitals were captured as normative probability maps, which were used to identify the disease risk and treatment-related latent factors. RESULTS: In contrast to case-control results, our normative modeling approach revealed highly individualized patterns of anatomic abnormalities in depressed patients (less than 11% extreme deviation overlapping for any regions). Based on our classification framework, models trained with individual normative probability maps (area under the receiver operating characteristic curve range, 0.7146-0.7836) showed better performance than models trained with original gray matter volume values (area under the receiver operating characteristic curve range, 0.6800-0.7036), which was verified in an independent external test set. Furthermore, different latent brain structural factors in relation to antidepressant treatment were revealed by a Bayesian model based on normative probability maps, suggesting distinct treatment response and inclination. CONCLUSIONS: Capturing personalized deviations from a normative range could help in understanding the heterogeneous neurobiology of depression and thus guide clinical diagnosis and treatment of depression.

Graph analysis of diffusion tensor imaging-based connectome in young men with internet gaming disorder.

Although recent evidence suggests that dysfunctional brain organization is associated with internet gaming disorder (IGD), the neuroanatomical alterations related to IGD remain unclear. In this diffusion tensor imaging (DTI) study, we aimed to examine alterations in white matter (WM) structural connectomes and their association with IGD characteristics in 47 young men with IGD and in 34 well-matched healthy controls. Two approaches [namely, network-based statistics (NBS) and graph theoretical measures] were applied to assess differences in the specific topological features of the networks and to identify the potential changes in the topological properties, respectively. Furthermore, we explored the association between the alterations and the severity of internet addiction. An NBS analysis revealed widespread alterations of the cortico-limbic-striatal structural connectivity networks in young people with IGD: (1) an increased subnet1 comprising the insula and the regions responsible for visual, auditory, and sensorimotor functions and (2) two decreased subnet2 and subnet3 comprising the insula, striatum, and limbic regions. Additional correlation analysis showed a significant positive association between the mean fractional anisotropy- (FA-) weighted connectivity strength of subnet1 and internet addiction test (IAT) scores in the IGD group. The present study extends our knowledge of the neuroanatomical correlates in IGD and highlights the role of the cortico-limbic-striatal network in understanding the neurobiological mechanisms underlying this disorder.

Predicting Treatment Selections for Individuals with Major Depressive Disorder According to Functional Connectivity Subgroups.

Background: Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. In this study, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations. Methods: All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery data set (n = 228), we first identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions. Results: Three subgroups with specific treatment recommendations were emerged, including (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities, (2) a stimulation-oriented subgroup with more alleviation in suicide, and (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing, respectively, conducted on three testing data sets, results showed an overall accuracy of 72.83%. Conclusions: Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes. Impact statement This study proposes a novel framework to provide treatment recommendations by integrating resting-state functional connectivity and advanced machine learning technique in a large data set. Our data-driven approach is able to objectively and automatically cluster patients into different subgroups and recommends the optimal treatment strategies based on specific brain circuits and clinical symptoms. Our results have the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

Individual Discriminative Ability of Resting Functional Brain Connectivity is Susceptible to the Time Span of MRI Scans.

Recent studies have suggested that resting-state brain functional connectivity (RSFC) has the potential to discriminate among individuals in a population. These studies mostly utilized a pattern of RSFC obtained from one scan to identify a given individual from the set of patterns obtained from the second scan. However, it remains unclear whether the discriminative ability would change with the extension of the time span between the two brain scans. This study explores the variations in the discriminative ability of RSFC on eight time spans, including 6 hours, 12 hours, 1 day, 1 month, 3-6 months, 7-12 months, 1-2 years and 2-3 years. We first searched for a set of the most discriminative RSFCs using the data of 200 healthy adult subjects from the Human Connectome Project dataset, and we then utilized this set of RSFCs to identify individuals from a population. The variations in the discriminative accuracies over different time spans were evaluated on datasets from a total of 682 unseen adult subjects acquired from four different sites. We found that although the accuracies were detectable at above-chance levels, the discriminative accuracies showed a significant decrease (F = 17.87, p < 0.01) along with the extension of brain imaging time span, from over 90% within one month to 66% at 2-3 years. Furthermore, the decreasing trend was robust and not dependent on the training set or analysis method. Therefore, we suggest that the discriminative ability of RSFC in identifying individuals should be susceptible to the length of time between brain scans.

Decreased Resting-State Functional Complexity in Elderly with Subjective Cognitive Decline.

Individuals with subjective cognitive decline (SCD) are at high risk of developing preclinical or clinical state of Alzheimer's disease (AD). Resting state functional magnetic resonance imaging, which can indirectly reflect neuron activities by measuring the blood-oxygen-level-dependent (BOLD) signals, is promising in the early detection of SCD. This study aimed to explore whether the nonlinear complexity of BOLD signals can describe the subtle differences between SCD and normal aging, and uncover the underlying neuropsychological implications of these differences. In particular, we introduce amplitude-aware permutation entropy (AAPE) as the novel measure of brain entropy to characterize the complexity in BOLD signals in each brain region of the Brainnetome atlas. Our results demonstrate that AAPE can reflect the subtle differences between both groups, and the SCD group presented significantly decreased complexities in subregions of the superior temporal gyrus, the inferior parietal lobule, the postcentral gyrus, and the insular gyrus. Moreover, the results further reveal that lower complexity in SCD may correspond to poorer cognitive performance or even subtle cognitive impairment. Our findings demonstrated the effectiveness and sensitiveness of the novel brain entropy measured by AAPE, which may serve as the potential neuroimaging marker for exploring the subtle changes in SCD.

Solid peroxides in Fenton-like reactions at near neutral pHs: Superior performance of MgO2 on the accelerated reduction of ferric species.

Fenton-like reactions at near neutral pHs are limited by the slow reduction of ferric species. Enhancing generation of from solid peroxides is a promising strategy to accelerate the rate-limiting step. Herein, the H2O2 release and Fenton-like reactions of four solid peroxides, MgO2, CaO2, ZnO2 and urea hydrogen peroxide (UHP), were investigated. Results indicated that UHP can release H2O2 instantly and show a similar behavior as H2O2 in the Fenton-like reactions. MgO2 released H2O2 quickly in phosphate buffered solutions, which was comparable to CaO2 but faster than ZnO2. Metal peroxides induced higher initial phenol degradation rates than UHP and H2O2 when the same theoretic H2O2 dosages and Fe(III)-EDTA were used. MgO2 displayed a superior performance for phenol degradation at pH 5, resulting in more than 93% phenol reduction at 1.5 h. According to kinetic analyses, the generation rate of in the MgO2 system was 18 and 3.4 times higher than those in ZnO2 and CaO2 systems, respectively. The addition of MgO2 significantly promoted H2O2 based Fenton-like reactions by increasing production of , and the mixture of MgO2 and H2O2 had an improved utilization efficiency of active oxygen than the MgO2 system. The findings suggested the critical roles of metal peroxides in favoring Fenton-like reactions and inspired strategies to simultaneously accelerate Fenton-like reactions and improve utilization efficiency of active oxygen.

Investigation on the Alteration of Brain Functional Network and Its Role in the Identification of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) is generally regarded as a prodromal stage of Alzheimer's disease (AD). In coping with the challenges caused by AD, we analyzed resting-state functional magnetic resonance imaging data of 82 MCI subjects and 93 normal controls (NCs). The alteration of brain functional network in MCI was investigated on three scales, including global metrics, nodal characteristics, and modular properties. The results supported the existence of small worldness, hubs, and community structure in the brain functional networks of both groups. Compared with NCs, the network altered in MCI over all the three scales. In scale I, we found significantly decreased characteristic path length and increased global efficiency in MCI. Moreover, altered global network metrics were associated with cognitive level evaluated by neuropsychological assessments. In scale II, the nodal betweenness centrality of some global hubs, such as the right Crus II of cerebellar hemisphere (CERCRU2.R) and fusiform gyrus (FFG.R), changed significantly and associated with the severity and cognitive impairment in MCI. In scale III, although anatomically adjacent regions tended to be clustered into the same module regardless of group, discrepancies existed in the composition of modules in both groups, with a prominent separation of the cerebellum and a less localized organization of community structure in MCI compared with NC. Taking advantages of random forest approach, we achieved an accuracy of 91.4% to discriminate MCI patients from NCs by integrating cognitive assessments and network analysis. The importance of the used features fed into the classifier further validated the nodal characteristics of CERCRU2.R and FFG.R could be potential biomarkers in the identification of MCI. In conclusion, the present study demonstrated that the brain functional connectome data altered at the stage of MCI and could assist the automatic diagnosis of MCI patients.

Structural-functional decoupling predicts suicide attempts in bipolar disorder patients with a current major depressive episode.

Bipolar disorder (BD) is associated with a high risk of suicidality, and it is challenging to predict suicide attempts in clinical practice to date. Although structural and functional connectivity alterations from neuroimaging studies have been previously reported in BD with suicide attempts, little is known about how abnormal structural and functional connectivity relates to each other. Here, we hypothesize that structure connectivity constrains functional connectivity, and structural-functional coupling is a more sensitive biomarker to detect subtle brain abnormalities than any single modality in BD patients with a current major depressive episode who had attempted suicide. By investigating structural and resting-state fMRI connectivity, as well as their coupling among 191 BD depression patients with or without a history of suicide attempts and 113 healthy controls, we found that suicide attempters in BD depression patients showed significantly decreased central-temporal structural connectivity, increased frontal-temporal functional connectivity, along with decreased structural-functional coupling compared with non-suicide attempters. Crucially, the altered structural connectivity network predicted the abnormal functional connectivity network profile, and the structural-functional coupling was significantly correlated with suicide risk but not with depression or anxiety severity. Our findings suggest that the structural connectome is the key determinant of brain dysfunction, and structural-functional coupling could serve as a valuable trait-like biomarker for BD suicidal predication over and above the intramodality network connectivity. Such a measure can have clinical implications for early identification of suicide attempters with BD depression and inform strategies for prevention.

Coupled use of Fe-impregnated biochar and urea-hydrogen peroxide to simultaneously reduce soil-air emissions of fumigant and improve crop growth.

Reducing the emissions of soil fumigants such as 1,3-dichloropropene (1,3-D) is essential to protecting air quality. Although biochar is useful in reducing such emissions, biochar-adsorbed fumigants may desorb and cause secondary air pollution. This study investigated the degradation of 1,3-D on iron (Fe)-impregnated biochar (FBC) amended with urea-hydrogen peroxide (UHP). The results indicated the degradation rate of trans-1,3-D on FBC-UHP was 54-fold higher than that on pristine biochar (PBC). Electron paramagnetic resonance (EPR) combined with other characterization methods revealed that the presence of semiquinone-type radicals in FBC effectively accelerated the Fe(III)/Fe(II) cycleto maintain enough Fe(IIII) for UHP activation and ·OH generation. ·OH, rather than ·O2-, was the dominant active oxidant. Soil column tests showed that application of FBC to the soil surface reduced cumulative 1,3-D emissions from 34.80 % (bare soil) to 0.81%. After the column experiment, the mixing of the FBC with UHP resulted in the residual cis-isomers decreasing from 32.5% to 10.5%. Greenhouse bioassays showed that mixing post-1,3-D degradation FBC-UHP with soil significantly promoted lettuce growth relative to PBC. The findings of this study provide a new approach for biochar application, especially for the emission reduction of hazardous volatile organic compounds from soil.

The role of soil organic matters and minerals on hydrogen peroxide decomposition in the soil.

Application of H2O2 in in-situ chemical oxidation (ISCO) for soil remediation has been limited by its rapid decomposition. However, effect of main factors involving in this phenomenon are not well understood. In this contribution, H2O2 decomposition in the six types of natural soils was investigated by kinetic analyses and soil characterizations. The grassland soil (GS) and red soil (RS) have the highest H2O2 decomposition rates (respective 0.048 and 0.069 min-1), while the paddy soil (PS) shows the lowest one (0.004 min-1). The decomposition mainly takes place on the surface adsorption sites of soil particles. PS has the highest content of SOM, which can block the active adsorption sites for H2O2 decomposition. The effects of dissolved organic matter (DOM) and biological debris in the soil are minor. Iron and manganese containing minerals are significantly influential on H2O2 decomposition, and the soil with a higher content of clay can induce faster H2O2 decomposition. The immobilized goethite (GM) and birnessite (BM) on montmorillonite were synthesized to simulate soil minerals. Results show H2O2 decomposition rates in BM is even faster than GM when the former dosage is two orders of magnitude lower than that of the latter. This indicates the crucial role of manganese minerals although their contents are generally much lower than that of iron in the soils. This study advanced the understanding of H2O2 decomposition in the soil and bring insights for H2O2 based ISCO technology in soil remediation.

Abnormal topology of brain functional networks in unipolar depression and bipolar disorder using optimal graph thresholding.

Two popular debilitating illness, unipolar depression (UD) and bipolar disorder (BD), have the similar symptoms and tight association on the psychopathological level, leading to a clinical challenge to distinguish them. In order to figure out the underlying common and different mechanism of both mood disorders, resting-state functional magnetic resonance imaging (rs-fMRI) data derived from 36 UD patients, 42 BD patients (specially type I, BD-I) and 45 healthy controls (HC) were analyzed retrospectively in this study. Functional brain networks were firstly constructed on both group and individual levels with a density 0.2, which was determined by a network thresholding approach based on modular similarity. Then we investigated the alterations of modular structure and other topological properties of the functional brain network, including global network characteristics and nodal network measures. The results demonstrated that the functional brain networks of UD and BD-I groups preserved the modularity and small-worldness property. However, compared with HC, reduced number of modules was observed in both patients' groups with shared alterations occurring in hippocampus, para hippocampal gyrus, amygdala and superior parietal gyrus and distinct changes of modular composition mainly in the caudate regions of basal ganglia. Additionally, for the network characteristics, compared to HC, significantly decreased global efficiency and small-worldness were observed in BD-I. For the nodal metrics, significant decrease of local efficiency was found in several regions in both UD and BD-I, while a UD-specified increase of participant coefficient was found in the right paracentral lobule and the right thalamus. These findings may contribute to throw light on the neuropathological mechanisms underlying the two disorders and further help to explore objective biomarkers for the correct diagnosis of UD and BD.

Cow manure-derived biochar: Its catalytic properties and influential factors.

The conversion of waste biomass into biochar is considered as a waste disposal alternative, especially because biochar is a low-cost adsorbent for soil contaminants. However, a risk of desorption of contaminants from biochar may lead to secondary pollution. This study investigated the degradation behavior of soil fumigant, 1,3-dichloropropne (1,3-D), on cow manure-derived biochar (CMB) pyrolyzed at five different temperatures from 300 to 700 °C (termed as C-300 to C-700). Results showed that 1,3-D degradation rate was U-shape related to biochar pyrolysis temperature. Four degradation byproducts (NH2CH2CH2CH3OH, CH3CH2NH2, NH2COCONH2, OHCH2COOH) were identified by headspace GC-MS. When biochar humidity improved from 0 to 50% or incubation temperature increased from 20 to 40 °C, the degradation of cis-1,3-D on C-300 improved 24.26% and 35.48%, respectively. The OH concentrations, detected by the terephthalic acid method, were considerably higher for C-300 than that for C-700. Pyrolysis temperature (300-700 °â€¯C) governed biochar physicochemical properties and further affected 1,3-D degradation mechanisms (pH-controlled substitution or OH-restricted oxidation reaction). All these findings showed that CMB can adsorb and degrade 1,3-D, thereby reduce its desorption risk, indicative of the conversion of cow manure into biochar as an effective waste management practice.

The Shared and Distinct White Matter Networks Between Drug-Naive Patients With Obsessive-Compulsive Disorder and Schizophrenia.

Background: Obsessive-compulsive disorder (OCD) and schizophrenia (SZ) as two severe mental disorders share many clinical symptoms, and have a tight association on the psychopathological level. However, the neurobiological substrates between these two diseases remain unclear. To the best of our knowledge, no study has directly compared OCD with SZ from the perspective of white matter (WM) networks. Methods: Graph theory and network-based statistic methods were applied to diffusion MRI to investigate and compare the WM topological characteristics among 29 drug-naive OCDs, 29 drug-naive SZs, and 65 demographically-matched healthy controls (NC). Results: Compared to NCs, OCDs showed the alterations of nodal efficiency and strength in orbitofrontal (OFG) and middle frontal gyrus (MFG), while SZs exhibited widely-distributed abnormalities involving the OFG, MFG, fusiform gyrus, heschl gyrus, calcarine, lingual gyrus, putamen, and thalamus, and most of these regions also showed a significant difference from OCDs. Moreover, SZs had significantly fewer connections in striatum and visual/auditory cortices than OCDs. The right putamen consistently showed significant differences between both disorders on nodal characteristics and structural connectivity. Conclusions: SZ and OCD present different level of anatomical impairment and some distinct topological patterns, and the former has more serious and more widespread disruptions. The significant differences between both disorders are observed in many regions involving the frontal, temporal, occipital, and subcortical regions. Particularly, putamen may serve as a potential imaging marker to distinguish these two disorders and may be the key difference in their pathological changes.

Rehabilitative compensatory mechanism of hierarchical subnetworks in major depressive disorder: A longitudinal study across multi-sites.

BACKGROUND: Brain structural connectome comprise of a minority of efficiently interconnected rich club nodes that are regarded as 'high-order regions'. The remission of major depressive disorder (MDD) in response to selective serotonin reuptake inhibitor (SSRI) treatment could be investigated by the hierarchical structural connectomes' alterations of subnetworks. METHODS: Fifty-five MDD patients who achieved remission underwent diffusion tensors imaging (DTI) scanning from 3 cohorts before and after 8-weeks antidepressant treatment. Five hierarchical subnetworks namely, rich, local, feeder, rich-feeder and feeder-local, were constructed according to the different combinations of connections and nodes as defined by rich club architecture. The critical treatment-related subnetwork pattern was explored by multivariate pattern analysis with support vector machine to differ the pre-/post-treatment patients. Then, relationships between graph metrics of discriminative subnetworks/ nodes and clinical variables were further explored. RESULTS: The feeder-local subnetwork presented the most discriminative power in differing pre-/post- treatment patients, while the rich-feeder subnetwork had the highest discriminative power when comparing pre-treatment patients and controls. Furthermore, based on the feeder connection, which indicates the information transmission between the core and non-core architectures of brain networks, its topological measures were found to be significantly correlated with the reduction rate of 17-item Hamilton Rating Scale for Depression. CONCLUSION: Although pathological lesion on MDD relied on abnormal core organization, disease remission was association with the compensation from non-core organization. These results suggested that the dysfunctions arising from hierarchical subnetworks are compensated by increased information interactions between core brain regions and functionally diverse regions.

Synthesis of a stable magnesium-impregnated biochar and its reduction of phosphorus leaching from soil.

Biochar improves soil fertility and promotes long-term terrestrial carbon sequestration. However, biochar seems not to be stable enough due to physical, chemical and biological reactions. In this study, a novel, stable, and magnesium (Mg)-impregnated biochar was prepared from cow dung and applied to decrease P leaching from soil. XPS, FTIR, XRD, SEM and EDS were used to evaluate the effect of modification and phosphorus(P) sorption on the oxidation resistance of biochar. The results showed that the oxidation resistance of the Mg-impregnated biochar was improved by the formation of MgO on its surface. The soil column experiment indicated that the Mg-impregnated -biochar decreased P loss from leaching by 89.25%. In addition, the available P content of the soil surface layer under Mg-impregnated biochar treatment increased by 3.5-fold relative to that under the control treatment. P sorption also enhanced the oxidation resistance of biochar. The relative contents of CO, CO, and COOH on the surface of P-laden biochar was 20.97% and was lower than those on the surface of biochar without P sorption (33.15%). Oxidation resistance was enhanced by the formation of new MgP crystals, which prevented the oxidation of CC, CC, and CH into CO, CO, and COOH, respectively, by acting as a physical barrier between the biochar surface and oxygen. The results of XRD, SEM and EDS provided evidence for the formation of MgP crystals. Overall, results indicated that the Mg-impregnated biochar can reduce P leaching loss from soil and has enhanced stability.

Abnormal dynamic community structure of the salience network in depression.

PURPOSE: To detect the consecutive variations of the internetwork interactions over time, which helps to discover the underlying dysfunction of depressive disorders. Abnormal interactions of resting-state functional networks have been reported in depression. However, little is known regarding the dynamics of how these crucial networks interact and the disease-related dysfunction. MATERIALS AND METHODS: Functional magnetic resonance imaging data at 3.0T in the resting state were acquired from 20 depressed patients and 20 healthy controls. Twelve resting-state networks were extracted by group-independent component analysis, and their interactions were calculated through a sliding windowed Granger causality model analysis. The acquired effective connectivity matrices were used to construct multislice networks with modular structures that were detected via a multislice community detection method. RESULTS: No significant differences were observed in the modularity and total module numbers between the depressed patients and the healthy controls. The P values were 0.133 with a confidence interval (-0.0001 0.0093) and 0.136 with a confidence interval (-0.30 0.90), respectively. However, the depressed patients exhibited decreased flexibility of the salience network (SN) compared with the controls (P = 0.048, corrected, with a confidence interval 0.0068 0.066). CONCLUSION: SN was inclined to participate less in the multiple brain functional modules across the resting time in depression, and infrequently changed its modular allegiance. These findings support the potential importance of the SN in the neuropathological mechanism of depression. LEVEL OF EVIDENCE: 1 J. Magn. Reson. Imaging 2017;45:1135-1143.

Reconfiguration of hub-level community structure in depressions: A follow-up study via diffusion tensor imaging.

BACKGROUND: The role of abnormal communications among large-scale brain networks have been given increasing attentions in the pathophysiology of major depressive disorder (MDD). However, few studies have investigated the effect of antidepressant medication treatment on the information communication of structural brain networks, especially converged from the individual analysis. METHODS: Nineteen unipolar MDD patients completed two diffusion tensor imaging (DTI) scans before and after 8-week treatment with selective serotonin reuptake inhibitor. DTI data of 37 matched healthy controls were acquired. We focused on a hub-level community structure network, and investigated whether it had differences on the whole structure and which regions drove these differences in terms of modular affiliation and hub role shift. Data were analyzed by the novel permutation network framework, which appraised the topological consistency of hubs and reserved an individual information. RESULTS: Compared to the pre-treatment state, post-treatment patients exhibited increasing number of modular members in the modules that included the right medial superior frontal gyrus (SFGmed) or the thalamus. Moreover, the result suggested a hub role shift of the left insula from a provincial-hub before treatment to a connector-hub after treatment. Additionally, reduced inter-module degree in the right SFGmed was positively correlated with the reduced sum score of 17-item Hamilton depression rating scale at the follow-up. CONCLUSIONS: Antidepressant medication treatment might be associated with modular reconfigurations of hubs within the fronto-limbic circuit. Moreover, increased inter-module connections of the left insula might improve its integration ability, promoting the remission of MDD. The correlation results of the right SFGmed suggested it might be a valuable indicator for treatment response.

Catalytic degradation of the soil fumigant 1,3-dichloropropene in aqueous biochar slurry.

Biochar has been explored as a cost-effective sorbent of contaminants, such as soil fumigant. However, contaminant-loaded biochar probably becomes a source of secondary air pollution. In this study, biochars developed from cow manure and rice husk at 300°C or 700°C were used to investigate the catalytic degradation of the soil fumigant 1,3-dichloropropene (1,3-D) in aqueous biochar slurry. Results showed that the adsorption of 1,3-D on the biochars was influenced by Langmuir surface monolayer adsorption. The maximum adsorption capacity of cow manure was greater than that of rice husk at the same pyrolysis temperature. Batch experiments revealed that 1,3-D degradation was improved in aqueous biochar slurry. The most rapid 1,3-D degradation occurred on cow manure-derived biochar produced at 300°C (C-300), with t1/2=3.47days. The degradation efficiency of 1,3-D on C-300 was 95.52%. Environmentally persistent free radicals (EPFRs) in biochars were detected via electron paramagnetic resonance (EPR) techniques. Dissolved organic matter (DOM) and hydroxyl radical (·OH) in biochars were detected by using a fluorescence spectrophotometer coupled with a terephthalic acid trapping method. The improvement of 1,3-D degradation efficiency may be attributed to EPFRs and DOM in aqueous biochar slurry. Our results may pose implications in the development of effective reduction strategies for soil fumigant emission with biochar.

Aberrant Global and Regional Topological Organization of the Fractional Anisotropy-weighted Brain Structural Networks in Major Depressive Disorder.

BACKGROUND: Most previous neuroimaging studies have focused on the structural and functional abnormalities of local brain regions in major depressive disorder (MDD). Moreover, the exactly topological organization of networks underlying MDD remains unclear. This study examined the aberrant global and regional topological patterns of the brain white matter networks in MDD patients. METHODS: The diffusion tensor imaging data were obtained from 27 patients with MDD and 40 healthy controls. The brain fractional anisotropy-weighted structural networks were constructed, and the global network and regional nodal metrics of the networks were explored by the complex network theory. RESULTS: Compared with the healthy controls, the brain structural network of MDD patients showed an intact small-world topology, but significantly abnormal global network topological organization and regional nodal characteristic of the network in MDD were found. Our findings also indicated that the brain structural networks in MDD patients become a less strongly integrated network with a reduced central role of some key brain regions. CONCLUSIONS: All these resulted in a less optimal topological organization of networks underlying MDD patients, including an impaired capability of local information processing, reduced centrality of some brain regions and limited capacity to integrate information across different regions. Thus, these global network and regional node-level aberrations might contribute to understanding the pathogenesis of MDD from the view of the brain network.